FAQ

With the Bleckwen credit fraud solution, we operate in a niche market without many agile competitors and are specialized enough to defend and maintain our position in the market. It is essential to mention that we do not replace traditional tools or internal data science teams. We complement existing processes to uncover this residual fraud that they can not. We have the best team equipped to succeed. In Bleckwen, we have a diverse international group of world-class data scientists with expertise in artificial intelligence and machine learning. AI is our core competency, and we have built a solid knowledge base of credit fraud with our current clients. We also have a unique history, having once been part of a large company before setting up on our own, which gives us a range of experience.

We have the experience and knowledge base of a large company while also having the agility and innovation of a Fintech company. And perhaps most unusual for a Fintech, our first client was BNP Paribas, the largest bank in Europe. That speaks volumes because we design for that size and BNP's demands. Many startups at our level could not sustain these demands and are not as scalable. BNP has played an essential role in our history and helped us develop our technology to deliver our services today. We know that we need to hire more salespeople and strengthen our marketing team to increase our presence in the Fintech ecosystem.

Bleckwen has experienced an exciting hyper-growth period in the last few months. During this time, we have signed RCI (Renault Bank) in France and launched a fraud assessment with RCI in Spain following our proven success in France. We achieved our 2021 growth targets, doubled the size of the sales team, and expanded our marketing activities during this period.

That depends on the client because we build custom models based on your business constraints and tolerance for false positives. So the term 'as expected' depends on your needs and requirements. Our goal is to detect at least 50% of the remaining fraud.

No. The models need enough data (24 to 36 months of historical data) to be well trained. If you are still doing manual checks to monitor your fraud, then our solution is not suitable for your needs at the moment. But in the meantime, we recommend you monitor on your historical data (e.g., suspected fraud, the definition of fraud in your business, distinguishing credit risk/loss to avoid mislabeling). This way, you can quickly implement our service once you have reached a sufficient volume.

We target an ROI of 400%.

There are multiple reasons why you should entrust the finding of fraud to Bleckwen rather than your internal data science team.

AI is a powerful mathematical model for using business knowledge to make better decisions. If you are still not convinced, we are happy to offer our residual fraud assessment, which allows measuring your internal residual fraud along with a projection of the residual copy, we will reduce thanks to our credit service. This residual fraud assessment has a value of 30,000 euros, but we would like to offer it free. We are sure of the strength of our solution. So this assessment does not commit you to anything, to any resources.

As we are a services company, we do not see any opportunity to advise our data science team, but we would be happy to make some recommendations.

Our service can detect residual fraud on individuals, and we are currently working on detecting fraud in businesses.

We do not analyze the documents themselves, but we can find inconsistencies in the information provided in the raw data. In the application, some records are fake; others are genuine. So we can detect the fraud with fake documents without the papers themselves.

There are two types of dashboards in the credit solution. The first is the operational dashboard that the customer currently has access to, including alerts and usage statistics. The second is the ML monitoring dashboard, which we now only use.

Customized models with explainability. Proven ROI and fast integration.

Yes, we accept all data in an anonymized format to comply with GDPR.

Specific anonymous data can affect the performance of the model.

Yes, fully GDPR compliant - anonymous data.

We supplement existing detection systems (e.g. KYC checks where a document contains performance).

That depends entirely on how the risk manager at your company defines fraud. Therefore, during our workshop with the risk manager, we must take some time to determine fraud in their company.

We base our models on an XG boost system. It's a call-out and a real-time API.

The API allows for great flexibility in transferring your credit files and our scoring. In addition, with this API, you are always in control and have a complete overview. The API also saves you valuable time and resources because otherwise, you would have to develop your internal system to communicate specifically with us about credit applications. Again, this would cost you several months and resources (money invested and time spent by your technical team) to implement a solution that does not provide any additional benefit other than being built by your technical team. The advantage is that we only work with structured data that is anonymized.

We monitor the models for variances, so it depends on the conflict - probably about every two months. Why you need to retrain - the drift issues arise when the model can no longer find new fraud patterns, as it is not incorporating these new patterns of fraud into its system. When the drift becomes apparent, we retrain the model. The frequency depends on the rise, so we monitor the importance, and if there is a noticeable drift, then the model needs to be retrained.

Even if you already have an internal tool, 88% of AI projects do not produce. We will also provide you with a residual fraud assessment (24 to 36 months) based on your historical data. You will also get a projection of the residual fraud reduction using our offering.